Biomass burning is one of the main sources of air pollution in South East Asia, predominantly during the dry period between June and October each year. Sumatra and Kalimantan, Indonesia, have been identified as the regions connected to biomass burning due to their involvement in agricultural activities. In Sumatra, the Province of Riau has always been found to have had the highest number of hotspots during haze episodes. This study aims to determine the concentration of five major pollutants (PM10, SO2, NO2, CO and O3) in Riau, Indonesia, for 2006 and 2007. It will also correlate the level of air pollutants to the number of hotspots recorded, using the hotspot information system introduced by the Malaysian Centre for Remote Sensing (MACRES). Overall, the concentration of air pollutants recorded was found to increase with the number of hotspots. Nevertheless, only the concentration of PM10 during a haze episode is significantly different when compared to its concentration in non-haze conditions. In fact, in August 2006, when the highest number of hotspots was recorded the concentration of PM10 was found to increase by more than 20% from its normal concentration. The dispersion pattern, as simulated by the Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT), showed that the distribution of PM10 was greatly influenced by the wind direction. Furthermore, the particles had the capacity to reach the Peninsular Malaysia within 42 hours of emission from the point sources as a consequence of the South West monsoon.
Environmental health in general is referring to the aspect of concern on healthy environment, and the interrelations between environment and human health. Due to the urbanization, urban development is changing the natural environment into a man-made environment. Along with the process, level of environmental quality and human health are decreased. Air quality as reference shows that urban ambient air is more polluted than rural. Due to high density of human population and their activities in urban areas, it produces air pollutants with higher rate as compared to less-developed areas. Air pollutants contribute to various health problems. People suffering from respiratory diseases are the most likely to be affected by air pollution. This paper aimed to examine the rate of respiratory infection among residents in an urban growth corridor (Petaling Jaya-Shah Alam-Klang) and the relationship with the urban land uses, traffic volume and air quality. There were four major types of data used in this study i.e., respiratory infection of the respondents, air quality, land use and traffic volume. A health questionnaire survey was carried out besides the secondary data collection from the various government departments. Relationship analysis was performed between respiratory health and the urban factors (air quality, traffic volume and land uses). The study found out that the relationship between the respiratory health and the urban factors is different in city-wide land use and traffic factors, as compared to the localised air quality and land use factors. To conclude, the urban factors are potentially affecting the respiratory health.
Biomass burning is one of the main sources of air pollution in South East Asia, predominantly during the dry period between June and October each year. Sumatra and Kalimantan, Indonesia, have been identified as the regions connected to biomass burning due to their involvement in agricultural activities. In Sumatra, the Province of Riau has always been found to have had the highest number of hotspots during haze episodes. This study aims to determine the concentration of five major pollutants (PM10, SO2, NO2, CO and O3) in Riau, Indonesia, for 2006 and 2007. It will also correlate the level of air pollutants to the number of hotspots recorded, using the hotspot information system introduced by the Malaysian Centre for Remote Sensing (MACRES). Overall, the concentration of air pollutants recorded was found to increase with the number of hotspots. Nevertheless, only the concentration of PM10 during a haze episode is significantly different when compared to its concentration in non-haze conditions. In fact, in August 2006, when the highest number of hotspots was recorded the concentration of PM10 was found to increase by more than 20% from its normal concentration. The dispersion pattern, as simulated by the Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT), showed that the distribution of PM10 was greatly influenced by the wind direction. Furthermore, the particles had the capacity to reach the Peninsular Malaysia within 42 hours of emission from the point sources as a consequence of the South West monsoon.
This paper provides detail on sequence analysis of hazy days based on eight monitoring stations from three states (Kelantan, Terengganu and Pahang) in the eastern region of Peninsular Malaysia. The dataset comprises of 1502 daily mean hazy days that had been measured for a decade. The meteorology data namely wind speed, wind direction, air temperature, relative humidity and particulate matter (PM10) were used to comprehend the variability, and the relationship existed amongst variables. The final dataset consists of a summary descriptive analysis and a boxplot, where all five variables were involved, including the minimum, maximum, mean, 1st quartile, median, 3rd quartile and standard deviation are presented. Apart from descriptive analysis, the normality test and histogram were performed as well.
Benzene, toluene, ethylbenzene and xylenes (BTEX) are well known hazardous volatile organic compounds (VOCs) due to their human health risks and photochemical effects. The main objective of this study was to estimate BTEX levels and evaluate interspecies ratios and ozone formation potentials (OFP) in the ambient air of urban Kuala Lumpur (KL) based on a passive sampling method with a Tenax® GR adsorbent tube. Analysis of BTEX was performed using a thermal desorption (TD)-gas chromatography mass spectrometer (GCMS). OFP was calculated based on the Maximum Incremental Reactivity (MIR). Results from this study showed that the average total BTEX during the sampling period was 66.06 ± 2.39 μg/m3. Toluene (27.70 ± 0.97 μg/m3) was the highest, followed by m,p-xylene (13.87 ± 0.36 μg/m3), o-xylene (11.49 ± 0.39 μg/m3), ethylbenzene (8.46 ± 0.34 μg/m3) and benzene (3.86 ± 0.31 μg/m3). The ratio of toluene to benzene (T:B) is > 7, suggesting that VOCs in the Kuala Lumpur urban environment are influenced by vehicle emissions and other anthropogenic sources. The average of ozone formation potential (OFP) value from BTEX was 278.42 ± 74.64 μg/m3 with toluene and xylenes being the major contributors to OFP. This study also indicated that the average of benzene concentration in KL was slightly lower than the European Union (EU)-recommended health limit value for benzene of 5 μg/m3 annual exposure.
Space is a resource that is constantly being depleted, especially in mega-cities. Underground workspaces (UGS) are increasingly being included in urban plans and have emerged as an essential component of vertical cities. While progress had been made on the engineering aspects associated with the development of high-quality UGS, public attitudes toward UGS as work environments (ie, the public's design concerns with UGS) are relatively unknown. Here, we present the first large-scale study examining preferences and attitudes toward UGS, surveying close to 2000 participants from four cities in three continents (Singapore, Shanghai, London, and Montreal). Contrary to previous beliefs, air quality (and not lack of windows) is the major concern of prospective occupants. Windows, temperature, and lighting emerged as additional important building performance aspects for UGS. Early adopters (ie, individuals more willing to accept UGS and thus more likely to be the first occupants) across all cities prioritized air quality. Present results suggest that (perceived) air quality is a key building performance aspect for UGS that needs to be communicated to prospective occupants as this will improve their attitudes and views toward UGS. This study highlights the importance of indoor air quality for the public.
Human livelihood highly depends on applying different sources of energy whose utilization is associated with air pollution. On the other hand, air pollution may be associated with glioblastoma multiforme (GBM) development. Unlike other environmental causes of cancer (e.g., irradiation), air pollution cannot efficiently be controlled by geographical borders, regulations, and policies. The unavoidable exposure to air pollution can modify cancer incidence and mortality. GBM treatment with chemotherapy or even its surgical removal has proven insufficient (100% recurrence rate; patient's survival mean of 15 months; 90% fatality within five years) due to glioma infiltrative and migratory capacities. Given the barrage of attention and research investments currently plowed into next-generation cancer therapy, oncolytic viruses are perhaps the most vigorously pursued. Provision of an insight into the current state of the research and future direction is essential for stimulating new ideas with the potentials of filling research gaps. This review manuscript aims to overview types of brain cancer, their burden, and different causative agents. It also describes why air pollution is becoming a concerning factor. The different opinions on the association of air pollution with brain cancer are reviewed. It tries to address the significant controversy in this field by hypothesizing the air-pollution-brain-cancer association via inflammation and atopic conditions. The last section of this review deals with the oncolytic viruses, which have been used in, or are still under clinical trials for GBM treatment. Engineered adenoviruses (i.e., DNX-2401, DNX-2440, CRAd8-S-pk7 loaded Neural stem cells), herpes simplex virus type 1 (i.e., HSV-1 C134, HSV-1 rQNestin34.5v.2, HSV-1 G207, HSV-1 M032), measles virus (i.e., MV-CEA), parvovirus (i.e., ParvOryx), poliovirus (i.e., Poliovirus PVSRIPO), reovirus (i.e., pelareorep), moloney murine leukemia virus (i.e., Toca 511 vector), and vaccinia virus (i.e., vaccinia virus TG6002) as possible life-changing alleviations for GBM have been discussed. To the best of our knowledge, this review is the first review that comprehensively discusses both (i) the negative/positive association of air pollution with GBM; and (ii) the application of oncolytic viruses for GBM, including the most recent advances and clinical trials. It is also the first review that addresses the controversies over air pollution and brain cancer association. We believe that the article will significantly appeal to a broad readership of virologists, oncologists, neurologists, environmentalists, and those who work in the field of (bio)energy. Policymakers may also use it to establish better health policies and regulations about air pollution and (bio)fuels exploration, production, and consumption.
Introduction During haze, at what level should Air Pollutant Index (API) showed, public
or private school be closed is not without controversy and is very much
debated. Therefore, the aim of this paper is to objectively quantify the
potential inhaled dose of PM10 associated with exposure at school and home
microenvironments during haze. The result of the health risk assessment will
be used to propose the API level for closing the school during haze episode.
Methods A hypothetical haze exposure scenario was created using the breakpoints of
PM10 concentration for calculation of API and respective inhaled dose during
haze. To determine the potential inhaled dose, we have considered many
factors that include time spent for specific physical intensity at school and
home microenvironments, age-specific and physical intensity-specific
inhalation rate (m3/min), and the indoor/outdoor ratio of PM10. To calculate
risk quotient (RQ), the inhaled dose was compared with the health reference
dose computed based on the concentration of PM10 in the Malaysian
Ambient Air Quality Standard.
Results When considering the specific exposure at each microenvironment (school
and home), the potential inhaled dose of PM10 was substantially lower when
school is closed for both primary and secondary school. The calculated risk
quotient (RQ) indicates that primary school children are likely to be affected
at slightly lower PM10 concentration (equivalent to API of 197) as compared
to secondary school children. Short duration of high physical activity
intensity during school breaks has contributed to a large proportion of inhaled
dose among school children indicating the important to avoid physical
activities during haze.
Conclusion Based on the assessment, taking into account the uncertainty of risk
assessment methodology, we proposed school to be closed when API reach
190 for both primary and secondary schools. These findings and
recommendations are only valid for naturally ventilated school and applicable
in the context of the current API calculation system and the existing
Recommended Air Quality Guideline values in Malaysia.
Dhaka and its neighboring areas suffer from severe air pollution, especially during dry season (November-April). We investigated temporal and directional variations in particulate matter (PM) concentrations in Dhaka, Gazipur, and Narayanganj from October 2012 to March 2015 to understand different aspects of PM concentrations and possible sources of high pollution in this region. Ninety-six-hour backward trajectories for the whole dry season were also computed to investigate incursion of long-range pollution into this area. We found yearly PM10 concentrations in this area about three times and yearly PM2.5 concentrations about six times greater than the national standards of Bangladesh. Dhaka and its vicinity experienced several air pollution episodes in dry season when PM2.5 concentrations were 8-13 times greater than the World Health Organization (WHO) guideline value. Higher pollution and great contribution of PM2.5 most of the time were associated with the north-westerly wind. Winter (November to January) was found as the most polluted season in this area, when average PM10 concentrations in Dhaka, Gazipur, and Narayanganj were 257.1, 240.3, and 327.4 μg m(-3), respectively. Pollution levels during wet season (May-October) were, although found legitimate as per the national standards of Bangladesh, exceeded WHO guideline value in 50 % of the days of that season. Trans-boundary source identifications using concentration-weighted trajectory method revealed that the sources in the eastern Indian region bordering Bangladesh, in the north-eastern Indian region bordering Nepal and in Nepal and its neighboring areas had high probability of contributing to the PM pollutions at Gazipur station.
Transboundary haze pollution caused by periodic forest fires has initiated a serious negative implication to the economy, tourism sectors and public health in Southeast Asia. Due to the raising concerns of health effects of haze towards the Malaysian population, the present study was conducted to investigate the degree of awareness towards haze and the number of safety measures adopted by the Malaysian population during the haze crisis based on demographic characteristics. A cross-sectional questionnaire-based study was conducted from 4 to 29 January 2016 on 387 subjects in Klang Valley, Malaysia. The results showed that more than 90% of the respondents were aware of the hazards of haze pollution. Respondents with higher education background with diploma, undergraduate and postgraduate degree possessed greater awareness on the hazards of haze (p RM 10,000/month) had higher awareness level on haze (p
The objective of the study is to examine the relationship between air pollution, fossil fuel energy consumption, water resources, and natural resource rents in the panel of selected Asia-Pacific countries, over a period of 1975-2012. The study includes number of variables in the model for robust analysis. The results of cross-sectional analysis show that there is a significant relationship between air pollution, energy consumption, and water productivity in the individual countries of Asia-Pacific. However, the results of each country vary according to the time invariant shocks. For this purpose, the study employed the panel least square technique which includes the panel least square regression, panel fixed effect regression, and panel two-stage least square regression. In general, all the panel tests indicate that there is a significant and positive relationship between air pollution, energy consumption, and water resources in the region. The fossil fuel energy consumption has a major dominating impact on the changes in the air pollution in the region.
Under the new development model, the digital economy has become a new engine to promote the green development of the economy and achieve the goal of "double carbon." Based on panel data from 30 Chinese provinces and cities from 2011 to 2021, the impact of the digital economy on carbon emissions was empirically studied by constructing a panel model and a mediation model. The results show that firstly, the effect of the digital economy on carbon emissions is a non-linear inverted "U" shaped relationship, and this conclusion still holds after a series of robustness tests; secondly, the results of the benchmark regression show that economic agglomeration is an essential mechanism through which the digital economy affects carbon emissions and that the digital economy can indirectly suppress carbon emissions through economic agglomeration. Finally, the results of the heterogeneity analysis show that the impact of the digital economy on carbon emissions varies according to the level of regional development, and its effect on carbon emissions is mainly in the eastern region, while its impact on the central and western regions is weaker, indicating that the impact effect is primarily in developed regions. Therefore, the government should accelerate the construction of new digital infrastructure and implement the development strategy of the digital economy according to local conditions to promote a more significant carbon emission reduction effect of the digital economy.
The creation of a welcoming hospital atmosphere is necessary to improve patient wellbeing and encourage healing. The goal of this study was to examine the variables affecting hospitalised patients' comfort. The study procedure included a thorough search of the Web of Science and Scopus databases, as well as the use of software analytic tools to graphically map enormous literature data, providing a deeper understanding of the linkages within the literature and its changing patterns. Insights from a range of disciplines, including engineering, psychology, immunology, microbiology, and environmental science, were included into our study using content analysis and clustering approaches. The physical environment and the social environment are two crucial factors that are related to patient comfort. The study stress the need of giving patient comfort a top priority as they heal, especially by tackling indoor air pollution. Our research also emphasises how important hospital care and food guidelines are for improving patient comfort. Prioritising patients who need specialised care and attention, especially those who have suffered trauma, should be the focus of future study. Future research in important fields including trauma, communication, hospital architecture, and nursing will be built on the findings of this study. To enhance research in these crucial areas, worldwide collaboration between experts from other nations is also advised. Although many studies stress the significance of patient comfort, few have drawn conclusions from a variety of disciplines, including medicine, engineering, immunology, microbiology, and environmental science, the most crucial issue of thoroughly researching the improvement of patient comfort has not been addressed. Healthcare workers, engineers, and other professions will benefit greatly from this study's investigation of the connection between hospital indoor environments and patient comfort.
This study aimed to investigate the chemical composition and potential sources of PM10 as well as assess the potential health hazards it posed to school children. PM10 samples were taken from classrooms at a school in Kuala Lumpur's city centre (S1) and one in the suburban city of Putrajaya (S2) over a period of eight hours using a low volume sampler (LVS). The composition of the major ions and trace metals in PM10 were then analysed using ion chromatography (IC) and inductively coupled plasma-mass spectrometry (ICP-MS), respectively. The results showed that the average PM10 concentration inside the classroom at the city centre school (82µg/m(3)) was higher than that from the suburban school (77µg/m(3)). Principal component analysis-absolute principal component scores (PCA-APCS) revealed that road dust was the major source of indoor PM10 at both school in the city centre (36%) and the suburban location (55%). The total hazard quotient (HQ) calculated, based on the formula suggested by the United States Environmental Protection Agency (USEPA), was found to be slightly higher than the acceptable level of 1, indicating that inhalation exposure to particle-bound non-carcinogenic metals of PM10, particularly Cr exposure by children and adults occupying the school environment, was far from negligible.
Matched MeSH terms: Air Pollutants/analysis; Air Pollution, Indoor/analysis*
This study was carried out to determine the concentration of lead (Pb), anions, and cations at six primary schools located around Kuala Lumpur. Low volume sampler (MiniVol PM10) was used to collect the suspended particulates in indoor and outdoor air. Results showed that the concentration of Pb in indoor air was in the range of 5.18 ± 1.08 μg/g-7.01 ± 0.08 μg/g. All the concentrations of Pb in indoor air were higher than in outdoor air at all sampling stations. The concentrations of cations and anions were higher in outdoor air than in indoor air. The concentration of Ca(2+) (39.51 ± 5.01 mg/g-65.13 ± 9.42 mg/g) was the highest because the cation existed naturally in soil dusts, while the concentrations of NO3 (-) and SO4 (2-) were higher in outdoor air because there were more sources of exposure for anions in outdoor air, such as highly congested traffic and motor vehicles emissions. In comparison, the concentration of NO3 (-) (29.72 ± 0.31 μg/g-32.00 ± 0.75 μg/g) was slightly higher than SO4 (2-). The concentrations of most of the parameters in this study, such as Mg(2+), Ca(2+), NO3 (-), SO4 (2-), and Pb(2+), were higher in outdoor air than in indoor air at all sampling stations.
Matched MeSH terms: Air Pollutants/analysis*; Air Pollution, Indoor/analysis*
This study aims to determine the composition and sources of particulate matter with an aerodynamic diameter of 10 μm or less (PM10) in a semi-urban area. PM10 samples were collected using a high volume sampler. Heavy metals (Fe, Zn, Pb, Mn, Cu, Cd and Ni) and cations (Na(+), K(+), Ca(2+) and Mg(2+)) were detected using inductively coupled plasma mass spectrometry, while anions (SO4 (2-), NO3 (-), Cl(-) and F(-)) were analysed using Ion Chromatography. Principle component analysis and multiple linear regressions were used to identify the source apportionment of PM10. Results showed the average concentration of PM10 was 29.5 ± 5.1 μg/m(3). The heavy metals found were dominated by Fe, followed by Zn, Pb, Cu, Mn, Cd and Ni. Na(+) was the dominant cation, followed by Ca(2+), K(+) and Mg(2+), whereas SO4 (2-) was the dominant anion, followed by NO3 (-), Cl(-) and F(-). The main sources of PM10 were the Earth's crust/road dust, followed by vehicle emissions, industrial emissions/road activity, and construction/biomass burning.
Matched MeSH terms: Air Pollutants/analysis*; Air Pollution/statistics & numerical data*
Forecasting higher than expected numbers of health events provides potentially valuable insights in its own right, and may contribute to health services management and syndromic surveillance. This study investigates the use of quantile regression to predict higher than expected respiratory deaths. Data taken from 70,830 deaths occurring in New York were used. Temporal, weather and air quality measures were fitted using quantile regression at the 90th-percentile with half the data (in-sample). Four QR models were fitted: an unconditional model predicting the 90th-percentile of deaths (Model 1), a seasonal/temporal (Model 2), a seasonal, temporal plus lags of weather and air quality (Model 3), and a seasonal, temporal model with 7-day moving averages of weather and air quality. Models were cross-validated with the out of sample data. Performance was measured as proportionate reduction in weighted sum of absolute deviations by a conditional, over unconditional models; i.e., the coefficient of determination (R1). The coefficient of determination showed an improvement over the unconditional model between 0.16 and 0.19. The greatest improvement in predictive and forecasting accuracy of daily mortality was associated with the inclusion of seasonal and temporal predictors (Model 2). No gains were made in the predictive models with the addition of weather and air quality predictors (Models 3 and 4). However, forecasting models that included weather and air quality predictors performed slightly better than the seasonal and temporal model alone (i.e., Model 3 > Model 4 > Model 2) This study provided a new approach to predict higher than expected numbers of respiratory related-deaths. The approach, while promising, has limitations and should be treated at this stage as a proof of concept.
Matched MeSH terms: Air Pollutants/toxicity; Air Pollution/adverse effects
The objective of this study is to identify spatial and temporal patterns in the air quality at three selected Malaysian air monitoring stations based on an eleven-year database (January 2000-December 2010). Four statistical methods, Discriminant Analysis (DA), Hierarchical Agglomerative Cluster Analysis (HACA), Principal Component Analysis (PCA) and Artificial Neural Networks (ANNs), were selected to analyze the datasets of five air quality parameters, namely: SO2, NO2, O3, CO and particulate matter with a diameter size of below 10 μm (PM10). The three selected air monitoring stations share the characteristic of being located in highly urbanized areas and are surrounded by a number of industries. The DA results show that spatial characterizations allow successful discrimination between the three stations, while HACA shows the temporal pattern from the monthly and yearly factor analysis which correlates with severe haze episodes that have happened in this country at certain periods of time. The PCA results show that the major source of air pollution is mostly due to the combustion of fossil fuel in motor vehicles and industrial activities. The spatial pattern recognition (S-ANN) results show a better prediction performance in discriminating between the regions, with an excellent percentage of correct classification compared to DA. This study presents the necessity and usefulness of environmetric techniques for the interpretation of large datasets aiming to obtain better information about air quality patterns based on spatial and temporal characterizations at the selected air monitoring stations.
Matched MeSH terms: Air Pollutants/analysis*; Air Pollution/analysis*
Methane (CH₄) emissions and oxidation were measured at the Air Hitam sanitary landfill in Malaysia and were modeled using the Intergovernmental Panel on Climate Change waste model to estimate the CH₄ generation rate constant, k. The emissions were measured at several locations using a fabricated static flux chamber. A combination of gas concentrations in soil profiles and surface CH₄ and carbon dioxide (CO₂) emissions at four monitoring locations were used to estimate the CH₄ oxidation capacity. The temporal variations in CH₄ and CO₂ emissions were also investigated in this study. Geospatial means using point kriging and inverse distance weight (IDW), as well as arithmetic and geometric means, were used to estimate total CH₄ emissions. The point kriging, IDW, and arithmetic means were almost identical and were two times higher than the geometric mean. The CH₄ emission geospatial means estimated using the kriging and IDW methods were 30.81 and 30.49 gm(−2) day(−1), respectively. The total CH₄ emissions from the studied area were 53.8 kg day(−1). The mean of the CH₄ oxidation capacity was 27.5 %. The estimated value of k is 0.138 year(−1). Special consideration must be given to the CH₄ oxidation in the wet tropical climate for enhancing CH₄ emission reduction.
Matched MeSH terms: Air Pollutants/analysis*; Air Pollution/statistics & numerical data
To meet the current diversified health needs in workplaces, especially in nonindustrial workplaces in developing countries, an indoor air quality (IAQ) component of a participatory occupational safety and health survey should be included.